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Machine Learning for Text Classification in Building Management Systems

Author(s): ORCID

ORCID
ORCID
Medium: journal article
Language(s): English
Published in: Journal of Civil Engineering and Management, , n. 5, v. 28
Page(s): 408-421
DOI: 10.3846/jcem.2022.16012
Abstract:

In building management systems (BMS), a medium building may have between 200 and 1000 sensor points. Their labels need to be translated into a naming standard so they can be automatically recognised by the BMS platform. The current industrial practices often manually translate these points into labels (this is known as the tagging process), which takes around 8 hours for every 100 points. We introduce an AI-based multi-stage text classification that translates BMS points into formatted BMS labels. After comparing five different techniques for text classification (logistic regression, random forests, XGBoost, multinomial Naive Bayes and linear support vector classification), we demonstrate that XGBoost is the top performer with 90.29% of true positives, and use the prediction confidence to filter out false positives. This approach can be applied in sensors networks in various applications, where manual free-text data pre-processing remains cumbersome.

Structurae cannot make the full text of this publication available at this time. The full text can be accessed through the publisher via the DOI: 10.3846/jcem.2022.16012.
  • About this
    data sheet
  • Reference-ID
    10679674
  • Published on:
    18/06/2022
  • Last updated on:
    18/06/2022
 
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